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  <h1>Source code for torch</h1><div class="highlight"><pre>
<span></span><span class="c1"># @lint-ignore-every PYTHON3COMPATIMPORTS</span>

<span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">The torch package contains data structures for multi-dimensional</span>
<span class="sd">tensors and mathematical operations over these are defined.</span>
<span class="sd">Additionally, it provides many utilities for efficient serializing of</span>
<span class="sd">Tensors and arbitrary types, and other useful utilities.</span>

<span class="sd">It has a CUDA counterpart, that enables you to run your tensor computations</span>
<span class="sd">on an NVIDIA GPU with compute capability &gt;= 3.0.</span>
<span class="sd">&quot;&quot;&quot;</span>

<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">sys</span>
<span class="kn">import</span> <span class="nn">platform</span>
<span class="kn">import</span> <span class="nn">ctypes</span>

<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">&lt;</span> <span class="p">(</span><span class="mi">3</span><span class="p">,):</span>
    <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Python 2 has reached end-of-life and is no longer supported by PyTorch.&quot;</span><span class="p">)</span>

<span class="kn">from</span> <span class="nn">._utils</span> <span class="kn">import</span> <span class="n">_import_dotted_name</span>
<span class="kn">from</span> <span class="nn">._utils_internal</span> <span class="kn">import</span> <span class="n">get_file_path</span><span class="p">,</span> <span class="n">prepare_multiprocessing_environment</span><span class="p">,</span> \
    <span class="n">USE_RTLD_GLOBAL_WITH_LIBTORCH</span>
<span class="kn">from</span> <span class="nn">.version</span> <span class="kn">import</span> <span class="n">__version__</span>
<span class="kn">from</span> <span class="nn">._six</span> <span class="kn">import</span> <span class="n">string_classes</span> <span class="k">as</span> <span class="n">_string_classes</span>

<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">&#39;typename&#39;</span><span class="p">,</span> <span class="s1">&#39;is_tensor&#39;</span><span class="p">,</span> <span class="s1">&#39;is_storage&#39;</span><span class="p">,</span> <span class="s1">&#39;set_default_tensor_type&#39;</span><span class="p">,</span>
    <span class="s1">&#39;set_rng_state&#39;</span><span class="p">,</span> <span class="s1">&#39;get_rng_state&#39;</span><span class="p">,</span> <span class="s1">&#39;manual_seed&#39;</span><span class="p">,</span> <span class="s1">&#39;initial_seed&#39;</span><span class="p">,</span> <span class="s1">&#39;seed&#39;</span><span class="p">,</span>
    <span class="s1">&#39;save&#39;</span><span class="p">,</span> <span class="s1">&#39;load&#39;</span><span class="p">,</span> <span class="s1">&#39;set_printoptions&#39;</span><span class="p">,</span> <span class="s1">&#39;chunk&#39;</span><span class="p">,</span> <span class="s1">&#39;split&#39;</span><span class="p">,</span> <span class="s1">&#39;stack&#39;</span><span class="p">,</span> <span class="s1">&#39;matmul&#39;</span><span class="p">,</span>
    <span class="s1">&#39;no_grad&#39;</span><span class="p">,</span> <span class="s1">&#39;enable_grad&#39;</span><span class="p">,</span> <span class="s1">&#39;rand&#39;</span><span class="p">,</span> <span class="s1">&#39;randn&#39;</span><span class="p">,</span>
    <span class="s1">&#39;DoubleStorage&#39;</span><span class="p">,</span> <span class="s1">&#39;FloatStorage&#39;</span><span class="p">,</span> <span class="s1">&#39;LongStorage&#39;</span><span class="p">,</span> <span class="s1">&#39;IntStorage&#39;</span><span class="p">,</span>
    <span class="s1">&#39;ShortStorage&#39;</span><span class="p">,</span> <span class="s1">&#39;CharStorage&#39;</span><span class="p">,</span> <span class="s1">&#39;ByteStorage&#39;</span><span class="p">,</span> <span class="s1">&#39;BoolStorage&#39;</span><span class="p">,</span>
    <span class="s1">&#39;DoubleTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;FloatTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;LongTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;IntTensor&#39;</span><span class="p">,</span>
    <span class="s1">&#39;ShortTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;CharTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;ByteTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;BoolTensor&#39;</span><span class="p">,</span> <span class="s1">&#39;Tensor&#39;</span><span class="p">,</span>
    <span class="s1">&#39;lobpcg&#39;</span><span class="p">,</span>
<span class="p">]</span>

<span class="c1">################################################################################</span>
<span class="c1"># Load the extension module</span>
<span class="c1">################################################################################</span>

<span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;Windows&#39;</span><span class="p">:</span>
    <span class="n">is_conda</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">prefix</span><span class="p">,</span> <span class="s1">&#39;conda-meta&#39;</span><span class="p">))</span>
    <span class="n">py_dll_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">exec_prefix</span><span class="p">,</span> <span class="s1">&#39;Library&#39;</span><span class="p">,</span> <span class="s1">&#39;bin&#39;</span><span class="p">)</span>
    <span class="n">th_dll_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="vm">__file__</span><span class="p">),</span> <span class="s1">&#39;lib&#39;</span><span class="p">)</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">th_dll_path</span><span class="p">,</span> <span class="s1">&#39;nvToolsExt64_1.dll&#39;</span><span class="p">))</span> <span class="ow">and</span> \
            <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">py_dll_path</span><span class="p">,</span> <span class="s1">&#39;nvToolsExt64_1.dll&#39;</span><span class="p">)):</span>
        <span class="n">nvtoolsext_dll_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
            <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">&#39;NVTOOLSEXT_PATH&#39;</span><span class="p">,</span> <span class="s1">&#39;C:</span><span class="se">\\</span><span class="s1">Program Files</span><span class="se">\\</span><span class="s1">NVIDIA Corporation</span><span class="se">\\</span><span class="s1">NvToolsExt&#39;</span><span class="p">),</span> <span class="s1">&#39;bin&#39;</span><span class="p">,</span> <span class="s1">&#39;x64&#39;</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">nvtoolsext_dll_path</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>

    <span class="kn">from</span> <span class="nn">.version</span> <span class="kn">import</span> <span class="n">cuda</span> <span class="k">as</span> <span class="n">cuda_version</span>
    <span class="kn">import</span> <span class="nn">glob</span>
    <span class="k">if</span> <span class="n">cuda_version</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">th_dll_path</span><span class="p">,</span> <span class="s1">&#39;cudart64*.dll&#39;</span><span class="p">)))</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> \
            <span class="nb">len</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">py_dll_path</span><span class="p">,</span> <span class="s1">&#39;cudart64*.dll&#39;</span><span class="p">)))</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
        <span class="n">cuda_version_1</span> <span class="o">=</span> <span class="n">cuda_version</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="s1">&#39;_&#39;</span><span class="p">)</span>
        <span class="n">cuda_path_var</span> <span class="o">=</span> <span class="s1">&#39;CUDA_PATH_V&#39;</span> <span class="o">+</span> <span class="n">cuda_version_1</span>
        <span class="n">default_path</span> <span class="o">=</span> <span class="s1">&#39;C:</span><span class="se">\\</span><span class="s1">Program Files</span><span class="se">\\</span><span class="s1">NVIDIA GPU Computing Toolkit</span><span class="se">\\</span><span class="s1">CUDA</span><span class="se">\\</span><span class="s1">v&#39;</span> <span class="o">+</span> <span class="n">cuda_version</span>
        <span class="n">cuda_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="n">cuda_path_var</span><span class="p">,</span> <span class="n">default_path</span><span class="p">),</span> <span class="s1">&#39;bin&#39;</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">cuda_path</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>

    <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">):</span>
        <span class="n">dll_paths</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">,</span> <span class="p">[</span><span class="n">th_dll_path</span><span class="p">,</span> <span class="n">py_dll_path</span><span class="p">,</span> <span class="n">nvtoolsext_dll_path</span><span class="p">,</span> <span class="n">cuda_path</span><span class="p">]))</span>

        <span class="k">for</span> <span class="n">dll_path</span> <span class="ow">in</span> <span class="n">dll_paths</span><span class="p">:</span>
            <span class="n">os</span><span class="o">.</span><span class="n">add_dll_directory</span><span class="p">(</span><span class="n">dll_path</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">is_conda</span> <span class="ow">or</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">&lt;</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">8</span><span class="p">):</span>
        <span class="n">dll_paths</span> <span class="o">=</span> <span class="p">[</span><span class="n">th_dll_path</span><span class="p">,</span> <span class="n">py_dll_path</span><span class="p">,</span> <span class="n">nvtoolsext_dll_path</span><span class="p">,</span> <span class="n">cuda_path</span><span class="p">]</span>
        <span class="n">dll_paths</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">,</span> <span class="n">dll_paths</span><span class="p">))</span> <span class="o">+</span> <span class="p">[</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;PATH&#39;</span><span class="p">]]</span>

        <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;PATH&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">dll_paths</span><span class="p">)</span>

    <span class="kn">import</span> <span class="nn">glob</span>
    <span class="n">dlls</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">th_dll_path</span><span class="p">,</span> <span class="s1">&#39;*.dll&#39;</span><span class="p">))</span>
    <span class="k">for</span> <span class="n">dll</span> <span class="ow">in</span> <span class="n">dlls</span><span class="p">:</span>
        <span class="n">ctypes</span><span class="o">.</span><span class="n">CDLL</span><span class="p">(</span><span class="n">dll</span><span class="p">)</span>


<span class="c1"># See Note [Global dependencies]</span>
<span class="k">def</span> <span class="nf">_load_global_deps</span><span class="p">():</span>
    <span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;Windows&#39;</span><span class="p">:</span>
        <span class="k">return</span>

    <span class="n">lib_name</span> <span class="o">=</span> <span class="s1">&#39;libtorch_global_deps&#39;</span> <span class="o">+</span> <span class="p">(</span><span class="s1">&#39;.dylib&#39;</span> <span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;Darwin&#39;</span> <span class="k">else</span> <span class="s1">&#39;.so&#39;</span><span class="p">)</span>
    <span class="n">here</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="vm">__file__</span><span class="p">)</span>
    <span class="n">lib_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">here</span><span class="p">),</span> <span class="s1">&#39;lib&#39;</span><span class="p">,</span> <span class="n">lib_name</span><span class="p">)</span>

    <span class="n">ctypes</span><span class="o">.</span><span class="n">CDLL</span><span class="p">(</span><span class="n">lib_path</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">ctypes</span><span class="o">.</span><span class="n">RTLD_GLOBAL</span><span class="p">)</span>


<span class="k">if</span> <span class="p">(</span><span class="n">USE_RTLD_GLOBAL_WITH_LIBTORCH</span> <span class="ow">or</span> <span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s1">&#39;TORCH_USE_RTLD_GLOBAL&#39;</span><span class="p">))</span> <span class="ow">and</span> \
        <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">!=</span> <span class="s1">&#39;Windows&#39;</span><span class="p">:</span>
    <span class="c1"># Do it the hard way.  You might want to load libtorch with RTLD_GLOBAL in a</span>
    <span class="c1"># few circumstances:</span>
    <span class="c1">#</span>
    <span class="c1">#   1. You&#39;re in a build environment (e.g., fbcode) where</span>
    <span class="c1">#      libtorch_global_deps is not available, but you still need</span>
    <span class="c1">#      to get mkl to link in with RTLD_GLOBAL or it will just</span>
    <span class="c1">#      not work.</span>
    <span class="c1">#</span>
    <span class="c1">#   2. You&#39;re trying to run PyTorch under UBSAN and you need</span>
    <span class="c1">#      to ensure that only one copy of libtorch is loaded, so</span>
    <span class="c1">#      vptr checks work properly</span>
    <span class="c1">#</span>
    <span class="c1"># If you&#39;re using this setting, you must verify that all the libraries</span>
    <span class="c1"># you load consistently use the same libstdc++, or you may have</span>
    <span class="c1"># mysterious segfaults.</span>
    <span class="c1">#</span>
    <span class="kn">import</span> <span class="nn">os</span> <span class="k">as</span> <span class="nn">_dl_flags</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">_dl_flags</span><span class="p">,</span> <span class="s1">&#39;RTLD_GLOBAL&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">_dl_flags</span><span class="p">,</span> <span class="s1">&#39;RTLD_LAZY&#39;</span><span class="p">):</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="c1"># next try if DLFCN exists</span>
            <span class="kn">import</span> <span class="nn">DLFCN</span> <span class="k">as</span> <span class="nn">_dl_flags</span>
        <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
            <span class="c1"># as a last attempt, use compile-time constants</span>
            <span class="kn">import</span> <span class="nn">torch._dl</span> <span class="k">as</span> <span class="nn">_dl_flags</span>
    <span class="n">old_flags</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">getdlopenflags</span><span class="p">()</span>
    <span class="n">sys</span><span class="o">.</span><span class="n">setdlopenflags</span><span class="p">(</span><span class="n">_dl_flags</span><span class="o">.</span><span class="n">RTLD_GLOBAL</span> <span class="o">|</span> <span class="n">_dl_flags</span><span class="o">.</span><span class="n">RTLD_LAZY</span><span class="p">)</span>
    <span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="o">*</span>
    <span class="n">sys</span><span class="o">.</span><span class="n">setdlopenflags</span><span class="p">(</span><span class="n">old_flags</span><span class="p">)</span>
    <span class="k">del</span> <span class="n">old_flags</span>
    <span class="k">del</span> <span class="n">_dl_flags</span>

<span class="k">else</span><span class="p">:</span>
    <span class="c1"># Easy way.  You want this most of the time, because it will prevent</span>
    <span class="c1"># C++ symbols from libtorch clobbering C++ symbols from other</span>
    <span class="c1"># libraries, leading to mysterious segfaults.</span>
    <span class="c1">#</span>
    <span class="c1"># See Note [Global dependencies]</span>
    <span class="n">_load_global_deps</span><span class="p">()</span>
    <span class="kn">from</span> <span class="nn">torch._C</span> <span class="kn">import</span> <span class="o">*</span>

<span class="n">__all__</span> <span class="o">+=</span> <span class="p">[</span><span class="n">name</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">_C</span><span class="p">)</span>
            <span class="k">if</span> <span class="n">name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;_&#39;</span> <span class="ow">and</span>
            <span class="ow">not</span> <span class="n">name</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;Base&#39;</span><span class="p">)]</span>

<span class="c1">################################################################################</span>
<span class="c1"># Define basic utilities</span>
<span class="c1">################################################################################</span>


<span class="k">def</span> <span class="nf">typename</span><span class="p">(</span><span class="n">o</span><span class="p">):</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">o</span><span class="o">.</span><span class="n">type</span><span class="p">()</span>

    <span class="n">module</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
    <span class="n">class_name</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">&#39;__module__&#39;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">!=</span> <span class="s1">&#39;builtins&#39;</span> \
            <span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">!=</span> <span class="s1">&#39;__builtin__&#39;</span> <span class="ow">and</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">module</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__module__</span> <span class="o">+</span> <span class="s1">&#39;.&#39;</span>

    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">&#39;__qualname__&#39;</span><span class="p">):</span>
        <span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__qualname__</span>
    <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="s1">&#39;__name__&#39;</span><span class="p">):</span>
        <span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__name__</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">class_name</span> <span class="o">=</span> <span class="n">o</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>

    <span class="k">return</span> <span class="n">module</span> <span class="o">+</span> <span class="n">class_name</span>


<div class="viewcode-block" id="is_tensor"><a class="viewcode-back" href="../torch.html#torch.is_tensor">[docs]</a><span class="k">def</span> <span class="nf">is_tensor</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns True if `obj` is a PyTorch tensor.</span>

<span class="sd">    Args:</span>
<span class="sd">        obj (Object): Object to test</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span></div>


<div class="viewcode-block" id="is_storage"><a class="viewcode-back" href="../torch.html#torch.is_storage">[docs]</a><span class="k">def</span> <span class="nf">is_storage</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns True if `obj` is a PyTorch storage object.</span>

<span class="sd">    Args:</span>
<span class="sd">        obj (Object): Object to test</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">obj</span><span class="p">)</span> <span class="ow">in</span> <span class="n">_storage_classes</span></div>


<div class="viewcode-block" id="set_default_tensor_type"><a class="viewcode-back" href="../torch.html#torch.set_default_tensor_type">[docs]</a><span class="k">def</span> <span class="nf">set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Sets the default ``torch.Tensor`` type to floating point tensor type</span>
<span class="sd">    ``t``. This type will also be used as default floating point type for</span>
<span class="sd">    type inference in :func:`torch.tensor`.</span>

<span class="sd">    The default floating point tensor type is initially ``torch.FloatTensor``.</span>

<span class="sd">    Args:</span>
<span class="sd">        t (type or string): the floating point tensor type or its name</span>

<span class="sd">    Example::</span>

<span class="sd">        &gt;&gt;&gt; torch.tensor([1.2, 3]).dtype    # initial default for floating point is torch.float32</span>
<span class="sd">        torch.float32</span>
<span class="sd">        &gt;&gt;&gt; torch.set_default_tensor_type(torch.DoubleTensor)</span>
<span class="sd">        &gt;&gt;&gt; torch.tensor([1.2, 3]).dtype    # a new floating point tensor</span>
<span class="sd">        torch.float64</span>

<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">_string_classes</span><span class="p">):</span>
        <span class="n">t</span> <span class="o">=</span> <span class="n">_import_dotted_name</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
    <span class="n">_C</span><span class="o">.</span><span class="n">_set_default_tensor_type</span><span class="p">(</span><span class="n">t</span><span class="p">)</span></div>


<div class="viewcode-block" id="set_default_dtype"><a class="viewcode-back" href="../torch.html#torch.set_default_dtype">[docs]</a><span class="k">def</span> <span class="nf">set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Sets the default floating point dtype to :attr:`d`. This type will be</span>
<span class="sd">    used as default floating point type for type inference in</span>
<span class="sd">    :func:`torch.tensor`.</span>

<span class="sd">    The default floating point dtype is initially ``torch.float32``.</span>

<span class="sd">    Args:</span>
<span class="sd">        d (:class:`torch.dtype`): the floating point dtype to make the default</span>

<span class="sd">    Example::</span>

<span class="sd">        &gt;&gt;&gt; torch.tensor([1.2, 3]).dtype           # initial default for floating point is torch.float32</span>
<span class="sd">        torch.float32</span>
<span class="sd">        &gt;&gt;&gt; torch.set_default_dtype(torch.float64)</span>
<span class="sd">        &gt;&gt;&gt; torch.tensor([1.2, 3]).dtype           # a new floating point tensor</span>
<span class="sd">        torch.float64</span>

<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">_C</span><span class="o">.</span><span class="n">_set_default_dtype</span><span class="p">(</span><span class="n">d</span><span class="p">)</span></div>

<span class="c1"># If you edit these imports, please update torch/__init__.py.in as well</span>
<span class="kn">from</span> <span class="nn">.random</span> <span class="kn">import</span> <span class="n">set_rng_state</span><span class="p">,</span> <span class="n">get_rng_state</span><span class="p">,</span> <span class="n">manual_seed</span><span class="p">,</span> <span class="n">initial_seed</span><span class="p">,</span> <span class="n">seed</span>
<span class="kn">from</span> <span class="nn">.serialization</span> <span class="kn">import</span> <span class="n">save</span><span class="p">,</span> <span class="n">load</span>
<span class="kn">from</span> <span class="nn">._tensor_str</span> <span class="kn">import</span> <span class="n">set_printoptions</span>

<span class="c1">################################################################################</span>
<span class="c1"># Define Storage and Tensor classes</span>
<span class="c1">################################################################################</span>

<span class="kn">from</span> <span class="nn">.tensor</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="kn">from</span> <span class="nn">.storage</span> <span class="kn">import</span> <span class="n">_StorageBase</span>


<span class="k">class</span> <span class="nc">DoubleStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">DoubleStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>


<div class="viewcode-block" id="FloatStorage"><a class="viewcode-back" href="../storage.html#torch.FloatStorage">[docs]</a><span class="k">class</span> <span class="nc">FloatStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">FloatStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span></div>


<span class="k">class</span> <span class="nc">HalfStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">HalfStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>


<span class="k">class</span> <span class="nc">LongStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">LongStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>


<span class="k">class</span> <span class="nc">IntStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IntStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>


<span class="k">class</span> <span class="nc">ShortStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">ShortStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>


<span class="k">class</span> <span class="nc">CharStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">CharStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>


<span class="k">class</span> <span class="nc">ByteStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">ByteStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>


<span class="k">class</span> <span class="nc">BoolStorage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">BoolStorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>


<span class="k">class</span> <span class="nc">BFloat16Storage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">BFloat16StorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>


<span class="k">class</span> <span class="nc">QUInt8Storage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">QUInt8StorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>

<span class="k">class</span> <span class="nc">QInt8Storage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">QInt8StorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>

<span class="k">class</span> <span class="nc">QInt32Storage</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">QInt32StorageBase</span><span class="p">,</span> <span class="n">_StorageBase</span><span class="p">):</span>
    <span class="k">pass</span>


<span class="n">_storage_classes</span> <span class="o">=</span> <span class="p">{</span>
    <span class="n">DoubleStorage</span><span class="p">,</span> <span class="n">FloatStorage</span><span class="p">,</span> <span class="n">LongStorage</span><span class="p">,</span> <span class="n">IntStorage</span><span class="p">,</span> <span class="n">ShortStorage</span><span class="p">,</span>
    <span class="n">CharStorage</span><span class="p">,</span> <span class="n">ByteStorage</span><span class="p">,</span> <span class="n">HalfStorage</span><span class="p">,</span> <span class="n">BoolStorage</span><span class="p">,</span> <span class="n">QUInt8Storage</span><span class="p">,</span> <span class="n">QInt8Storage</span><span class="p">,</span>
    <span class="n">QInt32Storage</span><span class="p">,</span> <span class="n">BFloat16Storage</span>
<span class="p">}</span>

<span class="c1"># The _tensor_classes set is initialized by the call to _C._initialize_tensor_type_bindings()</span>
<span class="n">_tensor_classes</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>


<span class="c1">################################################################################</span>
<span class="c1"># Initialize extension</span>
<span class="c1">################################################################################</span>

<span class="k">def</span> <span class="nf">manager_path</span><span class="p">():</span>
    <span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;Windows&#39;</span><span class="p">:</span>
        <span class="k">return</span> <span class="sa">b</span><span class="s2">&quot;&quot;</span>
    <span class="n">path</span> <span class="o">=</span> <span class="n">get_file_path</span><span class="p">(</span><span class="s1">&#39;torch&#39;</span><span class="p">,</span> <span class="s1">&#39;bin&#39;</span><span class="p">,</span> <span class="s1">&#39;torch_shm_manager&#39;</span><span class="p">)</span>
    <span class="n">prepare_multiprocessing_environment</span><span class="p">(</span><span class="n">get_file_path</span><span class="p">(</span><span class="s1">&#39;torch&#39;</span><span class="p">))</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Unable to find torch_shm_manager at &quot;</span> <span class="o">+</span> <span class="n">path</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">path</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span>


<span class="c1"># Shared memory manager needs to know the exact location of manager executable</span>
<span class="n">_C</span><span class="o">.</span><span class="n">_initExtension</span><span class="p">(</span><span class="n">manager_path</span><span class="p">())</span>
<span class="k">del</span> <span class="n">manager_path</span>

<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">_VariableFunctions</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">name</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;__&#39;</span><span class="p">):</span>
        <span class="k">continue</span>
    <span class="nb">globals</span><span class="p">()[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">_VariableFunctions</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>

<span class="c1">################################################################################</span>
<span class="c1"># Import interface functions defined in Python</span>
<span class="c1">################################################################################</span>

<span class="c1"># needs to be after the above ATen bindings so we can overwrite from Python side</span>
<span class="kn">from</span> <span class="nn">.functional</span> <span class="kn">import</span> <span class="o">*</span>


<span class="c1">################################################################################</span>
<span class="c1"># Remove unnecessary members</span>
<span class="c1">################################################################################</span>

<span class="k">del</span> <span class="n">DoubleStorageBase</span>
<span class="k">del</span> <span class="n">FloatStorageBase</span>
<span class="k">del</span> <span class="n">LongStorageBase</span>
<span class="k">del</span> <span class="n">IntStorageBase</span>
<span class="k">del</span> <span class="n">ShortStorageBase</span>
<span class="k">del</span> <span class="n">CharStorageBase</span>
<span class="k">del</span> <span class="n">ByteStorageBase</span>
<span class="k">del</span> <span class="n">BoolStorageBase</span>
<span class="k">del</span> <span class="n">QUInt8StorageBase</span>
<span class="k">del</span> <span class="n">BFloat16StorageBase</span>

<span class="c1">################################################################################</span>
<span class="c1"># Import most common subpackages</span>
<span class="c1">################################################################################</span>

<span class="kn">import</span> <span class="nn">torch.cuda</span>
<span class="kn">import</span> <span class="nn">torch.autograd</span>
<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="kn">import</span> <span class="n">no_grad</span><span class="p">,</span> <span class="n">enable_grad</span><span class="p">,</span> <span class="n">set_grad_enabled</span>
<span class="kn">import</span> <span class="nn">torch.nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.intrinsic</span>
<span class="kn">import</span> <span class="nn">torch.nn.quantized</span>
<span class="kn">import</span> <span class="nn">torch.optim</span>
<span class="kn">import</span> <span class="nn">torch.multiprocessing</span>
<span class="kn">import</span> <span class="nn">torch.sparse</span>
<span class="kn">import</span> <span class="nn">torch.utils.backcompat</span>
<span class="kn">import</span> <span class="nn">torch.onnx</span>
<span class="kn">import</span> <span class="nn">torch.jit</span>
<span class="kn">import</span> <span class="nn">torch.hub</span>
<span class="kn">import</span> <span class="nn">torch.random</span>
<span class="kn">import</span> <span class="nn">torch.distributions</span>
<span class="kn">import</span> <span class="nn">torch.testing</span>
<span class="kn">import</span> <span class="nn">torch.backends.cuda</span>
<span class="kn">import</span> <span class="nn">torch.backends.mkl</span>
<span class="kn">import</span> <span class="nn">torch.backends.mkldnn</span>
<span class="kn">import</span> <span class="nn">torch.backends.openmp</span>
<span class="kn">import</span> <span class="nn">torch.backends.quantized</span>
<span class="kn">import</span> <span class="nn">torch.quantization</span>
<span class="kn">import</span> <span class="nn">torch.utils.data</span>
<span class="kn">import</span> <span class="nn">torch.__config__</span>
<span class="kn">import</span> <span class="nn">torch.__future__</span>

<span class="n">_C</span><span class="o">.</span><span class="n">_init_names</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">_storage_classes</span><span class="p">))</span>

<span class="c1"># attach docstrings to torch and tensor functions</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_torch_docs</span><span class="p">,</span> <span class="n">_tensor_docs</span><span class="p">,</span> <span class="n">_storage_docs</span>
<span class="k">del</span> <span class="n">_torch_docs</span><span class="p">,</span> <span class="n">_tensor_docs</span><span class="p">,</span> <span class="n">_storage_docs</span>


<div class="viewcode-block" id="compiled_with_cxx11_abi"><a class="viewcode-back" href="../torch.html#torch.compiled_with_cxx11_abi">[docs]</a><span class="k">def</span> <span class="nf">compiled_with_cxx11_abi</span><span class="p">():</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1&quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">_GLIBCXX_USE_CXX11_ABI</span></div>


<span class="c1"># Import the ops &quot;namespace&quot;</span>
<span class="kn">from</span> <span class="nn">torch._ops</span> <span class="kn">import</span> <span class="n">ops</span>
<span class="kn">from</span> <span class="nn">torch._classes</span> <span class="kn">import</span> <span class="n">classes</span>

<span class="c1"># Import the quasi random sampler</span>
<span class="kn">import</span> <span class="nn">torch.quasirandom</span>

<span class="c1"># If you are seeing this, it means that this call site was not checked if</span>
<span class="c1"># the memory format could be preserved, and it was switched to old default</span>
<span class="c1"># behaviour of contiguous</span>
<span class="n">legacy_contiguous_format</span> <span class="o">=</span> <span class="n">contiguous_format</span>

<span class="c1"># Register fork handler to initialize OpenMP in child processes (see gh-28389)</span>
<span class="kn">from</span> <span class="nn">torch.multiprocessing._atfork</span> <span class="kn">import</span> <span class="n">register_after_fork</span>
<span class="n">register_after_fork</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">get_num_threads</span><span class="p">)</span>
<span class="k">del</span> <span class="n">register_after_fork</span>

<span class="c1"># Import tools that require fully imported torch (for applying</span>
<span class="c1"># torch.jit.script as a decorator, for instance):</span>
<span class="kn">from</span> <span class="nn">._lobpcg</span> <span class="kn">import</span> <span class="n">lobpcg</span>
</pre></div>

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